A framework for identifying affinity classes of inorganic materials binding peptide sequences

With the rapid development of bionanotechnology, there has been a growing interest recently in identifying the affinity classes of the inorganic materials binding peptide sequences. However, there are some distinct characteristics of inorganic materials binding sequence data that limit the performance of many widely-used classification methods. In this paper, we propose a novel framework to predict the affinity classes of peptide sequences with respect to an associated inorganic material. We first generate a large set of simulated peptide sequences based on our new amino acid transition matrix, and then the probability of test sequences belonging to a specific affinity class is calculated through solving an objective function. In addition, the objective function is solved through iterative propagation of probability estimates among sequences and sequence clusters. Experimental results on a real inorganic material binding sequence dataset show that the proposed framework is highly effective on identifying the affinity classes of inorganic material binding sequences.

Action research into social media within secondary school classes

Gail used social and participatory media in the secondary school classroom, not just to engage students but also to enhance their learning. Her action research successfully used concepts of formal and informal learning as well as peer review and peer/self-assessment. For the teacher, unlearning many previous practices became essential.

Safety and efficacy of antenatal milk expressing for women with diabetes in pregnancy: protocol for a randomised controlled trial

Many maternity providers recommend that women with diabetes in pregnancy express and store breast milk in late pregnancy so breast milk is available after birth, given (1) infants of these women are at increased risk of hypoglycaemia in the first 24 h of life; and (2) the delay in lactogenesis II compared with women without diabetes that increases their infant's risk of receiving infant formula. The Diabetes and Antenatal Milk Expressing (DAME) trial will establish whether advising women with diabetes in pregnancy (pre-existing or gestational) to express breast milk from 36 weeks gestation increases the proportion of infants who require admission to special or neonatal intensive care units (SCN/NICU) compared with infants of women receiving standard care. Secondary outcomes include birth gestation, breastfeeding outcomes and economic impact.

Identifying affinity classes of inorganic materials binding sequences via a graph-based model

Rapid advances in bionanotechnology have recently generated growing interest in identifying peptides that bind to inorganic materials and classifying them based on their inorganic material affinities. However, there are some distinct characteristics of inorganic materials binding sequence data that limit the performance of many widely-used classification methods when applied to this problem. In this paper, we propose a novel framework to predict the affinity classes of peptide sequences with respect to an associated inorganic material. We first generate a large set of simulated peptide sequences based on an amino acid transition matrix tailored for the specific inorganic material. Then the probability of test sequences belonging to a specific affinity class is calculated by minimizing an objective function. In addition, the objective function is minimized through iterative propagation of probability estimates among sequences and sequence clusters. Results of computational experiments on two real inorganic material binding sequence data sets show that the proposed framework is highly effective for identifying the affinity classes of inorganic material binding sequences. Moreover, the experiments on the structural classification of proteins (SCOP) data set shows that the proposed framework is general and can be applied to traditional protein sequences.